[Tensor] TP Linear 1D row (#843)

tests/test_tensor/test_linear_tp.py

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+import torch
+from colossalai.context.parallel_mode import ParallelMode
+from colossalai.tensor import ColoTensor
+
+from functools import partial
+
+import colossalai
+import pytest
+import torch
+import torch.multiprocessing as mp
+from colossalai.testing import parameterize, rerun_if_address_is_in_use
+from colossalai.utils.cuda import get_current_device
+from colossalai.utils import free_port
+from colossalai.core import global_context as gpc
+import torch.distributed as dist
+
+from test_tensor_utils import check_equal, replace_parameter_add_grad, broadcast_tensor_chunk
+
+def run_linear_tp1d_row_test():
+    device = get_current_device()
+    dtype = torch.float32
+    DEPTH = gpc.get_world_size(ParallelMode.PARALLEL_1D)
+    in_features = 4
+    out_features = 5
+
+    local_rank = gpc.get_local_rank(ParallelMode.PARALLEL_1D)
+
+    layer_master = torch.nn.Linear(in_features, out_features)
+    layer = torch.nn.Linear(in_features, out_features)
+
+    A_shape = (2, in_features)
+    A_master = torch.randn(A_shape, dtype=dtype, device=device)
+    A = broadcast_tensor_chunk(A_master, chunk_size=1)
+    A.requires_grad = True
+
+    W_shape = (out_features, in_features)
+    W_master = torch.randn(W_shape, dtype=dtype, device=device)
+    W = broadcast_tensor_chunk(W_master, chunk_size=DEPTH, local_rank=local_rank)
+    W.requires_grad = True
+
+    B_shape = (out_features)
+    B_master = torch.randn(B_shape, dtype=dtype, device=device)
+    B = broadcast_tensor_chunk(B_master, chunk_size=1)
+    B.requires_grad = True
+
+    # replace the torch nn.Parameters with ColoTensor
+    sharded_weight = ColoTensor.init_from_torch_tensor(W)
+    sharded_weight._shard_spec = "1Drow"
+    sharded_bias = ColoTensor.init_from_torch_tensor(B)
+    replace_parameter_add_grad(layer, sharded_weight, sharded_bias)
+    out = layer(A)
+
+    replace_parameter_add_grad(layer_master, W_master, B_master)
+    A_master.requires_grad = True
+    #C_master = torch.matmul(A_master, W_master.transpose(0, 1)) + B_master
+    C_master = layer_master(A_master)
+    C = C_master.clone()
+
+    check_equal(out, C)
+
+    grad_shape = C_master.shape
+    grad_master = torch.randn(grad_shape, dtype=dtype, device=get_current_device())
+    grad = broadcast_tensor_chunk(grad_master, chunk_size=1)
+    out.backward(grad)
+
+    grad_master = grad_master.clone()
+    C_master.backward(grad_master)
+
+    W_grad = W_master.grad
+    W_grad = torch.chunk(W_grad, DEPTH, dim=-1)[local_rank]
+    check_equal(W_grad, layer.weight.grad)
+
+    B_grad = B_master.grad
+    check_equal(B_grad, layer.bias.grad)
+
+def run_dist(rank, world_size, port):
+    config = dict(parallel=dict(tensor=dict(mode="1d", size=world_size),))
+    colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    run_linear_tp1d_row_test()
+
+
+@pytest.mark.dist
+@parameterize('world_size', [1, 4])
+@rerun_if_address_is_in_use()
+def test_linear_1d(world_size):
+    run_func = partial(run_dist, world_size=world_size, port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_linear_1d()

tests/test_tensor/test_tensor_utils/__init__.py

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+from ._util import *

tests/test_tensor/test_tensor_utils/_util.py

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+import torch
+import torch.distributed as dist
+
+def check_equal(A, B):
+    assert torch.allclose(A, B, rtol=1e-3, atol=1e-1) == True
+
+def replace_parameter_add_grad(layer, weight=None, bias=None):
+    if weight is not None:
+        delattr(layer, 'weight')
+        setattr(layer, 'weight', weight)
+        layer.weight.requires_grad = True
+    if bias is not None:
+        delattr(layer, 'bias')
+        setattr(layer, 'bias', bias)
+        layer.bias.requires_grad = True
+
+def broadcast_tensor_chunk(tensor, chunk_size=1, local_rank=0):
+    dist.broadcast(tensor, src=0)
+    tensor_chunk = torch.chunk(tensor, chunk_size, dim=-1)[local_rank]
+    return tensor_chunk.clone()